Systems and methods for extracting media from network traffic having unknown protocols

ABSTRACT

Methods and systems for analyzing network traffic. An analysis system receives network traffic, which complies with a certain protocol. The received network traffic carries a data item, which may be of value to an analyst. In order to access the data item in question, the analysis system automatically identifies the media type of the data item, by processing the network traffic irrespective of the protocol. The analysis system identifies the media type irrespective of the protocol in order to avoid the computational complexity involved in decoding the protocol.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to communication analysis, and particularly to methods and systems for identifying media in network traffic.

BACKGROUND OF THE DISCLOSURE

Various systems and applications are used for exchanging data over communication networks, such as the Internet. Such systems and applications may use various kind of protocols, and may carry data of various media types, such as text, audio, still images or video.

SUMMARY OF THE DISCLOSURE

An embodiment that is described herein provides a method, including:

receiving in a computerized analysis system network traffic, which complies with a protocol and which carries a data item of a respective media type;

automatically identifying the media type by processing the network traffic irrespective of the protocol; and

extracting at least part of the data item responsively to the identified media type.

In some embodiments, the protocol is not decodable by the analysis system. In an embodiment, the data item includes a sequence of bytes, and identifying the media type includes identifying that the sequence of bytes includes valid text. Identifying the valid text may include identifying a character set that is used for constructing the sequence of bytes. In another embodiment, identifying the valid text includes verifying at least one criterion selected from a group of criteria consisting of:

the sequence is longer than a predefined minimal length;

at least a predefined portion of the bytes in the sequence represent letters;

the sequence includes only bytes representing valid displayable characters; and

the sequence includes at least one consecutive run of multiple alphanumerical characters.

In a disclosed embodiment, identifying the valid text includes assessing a statistical property of the bytes, and identifying that the text is valid responsively to the assessed statistical property. Assessing the statistical property may include assessing an occurrence frequency of a given character in the sequence of the bytes. Additionally or alternatively, assessing the statistical property may include assessing a measure of randomness of the sequence of the bytes. Further additionally or alternatively, assessing the statistical property may include comparing a first statistical distribution of the bytes in the sequence with a second statistical distribution of characters in a given language.

In some embodiments, identifying the media type includes identifying in the network traffic a file type that is associated with the media type. In an embodiment, identifying the file type includes detecting a byte pattern that is characteristic of the file type. In a disclosed embodiment, the data item includes a sequence of bytes, and identifying the media type includes identifying that the sequence of bytes includes visual information.

In another embodiment, the data item includes a sequence of bytes, and identifying the media type includes identifying that the sequence of bytes includes digitized audio. In still another embodiment, the data item includes a sequence of bytes, and identifying the media type includes identifying that the sequence of bytes includes encrypted information. In an embodiment, the data item includes a sequence of bytes, and identifying the media type includes identifying that the sequence of bytes includes compressed information. In an embodiment, extracting the data item includes selecting a modality for presenting the data item responsively to the identified media type, and presenting the data item to an operator using the selected modality.

There is additionally provided, in accordance with an embodiment that is described herein, apparatus, including:

an interface configured to receive network traffic, which complies with a protocol and which carries a data item of a respective media type; and

a processor, which is configured to automatically identify the media type by processing the network traffic irrespective of the protocol, and to extract at least part of the data item responsively to the identified media type.

The present disclosure will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that schematically illustrates a system for analyzing network traffic, in accordance with an embodiment of the present disclosure; and

FIG. 2 is a flow chart that schematically illustrates a method for extracting media from network traffic having an unknown protocol, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS Overview

Data that is sent over a communication network often comprises data items of various media types, such as text, audio and visual information (e.g., images or video). In some cases, parts of the data may be encrypted. The data is typically sent over the network using a certain application-layer protocol, such as the Hyper-Text Transfer Protocol (HTTP), File Transfer Protocol (FTP) or Real Time Protocol (RTP), to name just a few examples. The receiving party typically decodes the application-layer protocol in order to determine the media type or types and extract the data.

In some applications, an external system (or other third party that is not one of the designated participants in the communication) attempts to analyze (decode) the network traffic. The external system may be unable to complete the task, since the protocols and the applications involved may not be known to it. For example, an analysis system may receive network traffic from a communication network and attempt to extract and analyze the data conveyed by the traffic. In some cases, the analysis system may not be able to decode a given protocol, e.g., because the system does not support or does not recognize the protocol.

Embodiments that are described herein provide improved methods and systems for analyzing network traffic. In some embodiments, an analysis system is connected to a communication network, such as the Internet. The analysis system receives network traffic, which complies with a certain protocol. The received network traffic carries a data item, which may be of value to an analyst.

In order to access the data item in question, the analysis system automatically identifies the media type of the data item, by processing the network traffic irrespective of the protocol. In some embodiments, the protocol is not decodable by the analysis system. In other embodiments, the analysis system identifies the media type irrespective of the protocol in order to avoid the computational complexity involved in decoding the protocol.

Several example methods for identifying text, image and/or encrypted traffic information are described herein. The disclosed techniques typically regard the network traffic as a stream of bytes, and attempt to detect in the stream characteristics that are indicative of the media type. As such, these techniques do not rely on knowledge of the protocol. Having identified the media type of the data item, the analysis system extracts at least part of the data item and presents it accordingly.

The methods and systems described herein provide the ability to automatically analyze, prioritize and present large volumes of network traffic, even for unsupported or unrecognized protocols. Rather than providing the analyst with a raw stream of bytes, the extracted data can be presented in a manner that matches its media type.

System Description

FIG. 1 is a block diagram that schematically illustrates a computerized system 20 for analyzing network traffic, in accordance with an embodiment of the present disclosure. System 20 is connected to a communication network 28, and receives from network 28 network traffic that is exchanged between network users 24. Network 28 may comprise a Wide-Area Network (WAN) such as the Internet, or any other suitable network. Typically although not necessarily, network 28 comprises an Internet Protocol (IP) network.

Users 24 communicate over network 28 using various protocols. Example protocols are various proprietary protocols used by peer-to-peer applications (e.g., eMule or BitTorent), gaming applications and chat applications. Other example protocols are the Hyper-Text Transfer Protocol (HTTP), File Transfer Protocol (FTP), Real Time Protocol (RTP), Transmission Control Protocol (TCP) or User Datagram Protocol (UDP). Alternatively, any other suitable protocol can also be used. Using these protocols, users 24 exchange various kinds of data items of various media types. Example media types that can be used are text, audio, visual information (e.g., still images or video), encrypted content, or any other suitable media type.

System 20 receives communication traffic (e.g., communication packets) from network 28, and analyzes the traffic in order to extract information that is of value. In particular, system 20 automatically identifies the media types of data items in the received traffic irrespective of the underlying protocol, using methods that are described below. Systems of this sort can be used, for example, in test equipment, network probes, Quality-of-Service (QoS) systems, Digital Rights Management (DRM) systems, or in any other suitable system or application.

System 20 comprises a network interface 32 for receiving the network traffic from network 28, and a processor 36 that carries out the methods described herein. In some embodiments, system 20 extracts the data items in accordance with the identified media types. The extracted data items are presented to an operator 40 using a suitable output device, such as a display 44. (When the data items comprise audio, the output device may comprise a speaker or headset, for example.)

The system configuration shown in FIG. 1 is an example configuration, which is chosen purely for the sake of conceptual clarity. In alternative embodiments, any other suitable system configuration can also be used. For example, the functions of system 20 can be integrated with other analysis functions. Certain elements of system 20 can be implemented using hardware, such as using one or more Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs). Additionally or alternatively, certain elements of system 20 can be implemented using software, or using a combination of hardware and software elements.

Typically, processor 36 comprises a general-purpose computer, which is programmed in software to carry out the functions described herein. The software may be downloaded to the computer in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.

Media Type Identification Methods

Identifying the media type of a data item irrespective of the protocol can be advantageous in various scenarios. In some practical cases, system 20 receives network traffic, which complies with a protocol that is not decodable by the system. For example, system 20 may not support decoding of one or more protocols that are used in network 28. In other cases, system 20 may fail to recognize a certain protocol, even if it supports its decoding. Further alternatively, system 20 may be unable to decode a certain protocol for any other reason.

In other cases, the protocol is decodable by system 20, but the system nevertheless identifies the media type without decoding the protocol (or at least without fully decoding the protocol). This technique reduces or eliminates the computational complexity that is associated with decoding the protocol, and therefore improves the system performance. For example, refraining from decoding the protocol can enable the system to analyze considerably higher traffic volumes.

In the embodiments described herein, the non-decodable protocol in question comprises an application-layer protocol, i.e., a protocol that is associated with layer 7 of the Open System Interconnection (OSI) reference model. Example application-layer protocols comprise HTTP, FTP and RTP, among others. In alternative embodiments, the protocol is associated with any layer that is higher than the transport layer, i.e., higher than layer 4 of the OSI model. In some scenarios, a user may attempt to hide the real application he or she is using, by communicating through proxies. In this sort of scenario, the traffic may appear to be HTTP traffic when in fact it is associated with a different protocol or application. Further alternatively, the protocol may comprise any other suitable protocol.

In some embodiments, processor 36 automatically identifies the media types of respective data items in the received network traffic, irrespective of the protocols that are used for sending the traffic. Typically although not necessarily, processor 36 regards the network traffic of a certain flow or connection as a stream of bytes. Processor 36 attempts to find in the byte stream characteristics that are indicative of a certain media type (e.g., textual information, image information or encrypted communication), and identifies the media types using these characteristics. Alternatively, the processor may use any other method that automatically identifies the media type of a given data item irrespective of the protocol that is used to send it.

FIG. 2 is a flow chart that schematically illustrates a method for extracting media from network traffic having an unknown protocol, in accordance with an embodiment of the present disclosure. The method begins with system 20 receiving (from network 28 via interface 32) network traffic, which complies with a protocol that is not decodable by the system, at an input step 50. Processor 36 processes the network traffic, so as to automatically identify the media types of respective data items in the traffic, at a processing step 54. As will be explained and demonstrated below, this processing is performed irrespective of the non-decodable protocol with which the traffic complies. Processor 36 extracts the data items from the network traffic, in accordance with the automatically-identified media types, at an extraction step 58. The processor presents each data item to operator 44 in accordance with its respective media type, at an output step 62.

Processor 36 may use various techniques for automatically identifying the media types of data items irrespective of the underlying protocol. For example, processor 36 may apply certain criteria to the network traffic, in order to identify segments of the traffic that contain valid textual information. Consider, for example, a segment containing a sequence of American Standard Code for Information Interchange (ASCII) characters. In some embodiments, processor 36 identifies such a segment as valid if it meets one or more filtering criteria. Filtering criteria may regard the segment as valid, for example, if the segment is longer than a predefined minimal length, if the segment contains at least a predefined number or ratio of letters (as opposed to digits and other text characters), if the segment contains only valid displayable characters, and/or if the segment contains at least one consecutive run of alphanumerical characters (letters or digits). Additionally or alternatively, any other suitable filtering criteria can be used.

Additionally or alternatively, processor 36 may compute a statistical property of the byte values in the segment, and regard the segment as valid only if the statistical property is met. For example, processor 36 may regard the segment as valid text if the occurrence frequency of commas, space characters and/or other characters is within a specified range. As another example, processor 36 may regard the segment as valid text if the byte values in the segment are sufficiently random, e.g., sufficiently distributed over the range of possible values. A long sequence of the letter “A”, for example, will not be considered valid text according to the latter criterion.

As yet another example of statistical analysis, processor 36 may have a-priori knowledge as to the statistical distribution of text character values in different languages. In some embodiments, the processor assesses the statistical distribution (e.g., histogram) of byte values in the segment, and regards the segment as valid text if the assessed distribution matches the known distribution of a given language. In an embodiment, processor 36 may be configured to expect a particular language, and therefore regard a segment as valid text if the assessed distribution matches the distribution of that particular language.

In practice, different protocols may represent textual information using various character sets, such as ASCII, 8-bit Unicode Transformation Format (UTF-8), 16-bit UTF (UTF-16), Windows-1255, or any other suitable character set. In some embodiments, processor 36 identifies a traffic segment as containing text if all the byte values in the segment comprise valid character values of a given character set. This technique is useful, for example, when the character set uses byte values that all fall in a specific range of the possible byte values.

In some cases, however, a given character set uses byte values that are distributed over the entire range of possible values (e.g., 0-255). In such cases, additional criteria, such as language-dependent criteria, may be used to identify valid text segments.

For example, processor 36 may assume that a valid text segment is written in a single language. Therefore, if a segment is found to contain one or more characters that are specific to a certain language, and also one or more characters that are specific to another language, processor 36 may regard this segment as unlikely to be valid text. Note that certain characters may be specific to two or more languages. Detecting a character of this sort may indicate that the segment is written in one of the languages in which the detected character is defined. In some embodiments, processor 36 is configured to search for one or more specific languages. When attempting to determine a valid text segment, the processor may attempt to match the character set and/or statistical properties of the byte values only to those specific languages.

Additionally or alternatively, processor 36 may filter a segment of a given character set (e.g., UTF-8 or UTF-16) by applying any of the filtering criteria that were described above with respect to the ASCII character set.

The description above referred to automatic identification of textual information in the network traffic, irrespective of the underlying protocol. Additionally or alternatively, processor 36 may automatically identify other media types, such as digitized audio or visual information (still images or video). In some embodiments, the processor automatically identifies a certain file type, which is associated with a respective media type. The processor may identify the file type, for example, by detecting a character sequence or pattern that is characteristic of that file type, or using any other suitable technique.

Consider, for example, JPEG files containing compressed images. A JPEG file typically comprises a header containing the character sequence (“JFIF”). Upon detecting this character sequence, processor 36 may deduce that the traffic in question comprises a JPEG file. The processor may synchronize with the file header using the identified “JFIF” sequence, extract the image attributes (e.g., image size), and extract the image media (content) from the file. The processor may also verify whether the extracted media indeed complies with the JPEG format.

In some embodiments, processor 36 automatically identifies data items comprising encrypted information. In the present context, encrypted information is also regarded as a distinct media type. The processor may identify encrypted content, for example, by examining the entropy level (or other measure of randomness) of the byte stream. In some embodiments, processor 36 automatically identifies data items comprising compressed information (e.g., using GZIP or other compression technique). In other words, compressed data may also be regarded as a media type.

The above-described techniques are provided herein purely by way of example. In alternative embodiments, processor 36 may use any other method to automatically identify the media type of respective traffic segments, irrespective of the protocols used to send these segments. In some embodiments, processor 36 is constructed in a modular configuration for identifying multiple media types. For example, processor 36 may comprise multiple plug-in software modules, each plug-in module configured to identify a specific media type. Typically, plug-in modules can be added or removed as appropriate.

In some embodiments, system 20 presents each segment to operator 40 using a modality that matches its respective identified media type. Typically, processor 36 selects a modality for presenting the segment responsively to the identified media type of the segment, and presents the data item to the operator using the selected modality. For example, traffic segments identified as text are displayed alphanumerically on display 44. Traffic segments identified as still images or video are displayed graphically on display 44 according to their identified formats. Traffic segments identified as audio are sounded to operator 40 using a suitable speaker or headset (not shown in the figures).

As a result, the operator is able to access useful information in the identified segments in an efficient manner. Without the disclosed techniques, the operator would need to manually review streams of bytes belonging to non-decodable protocols. Such a manual process is extremely time-consuming, error-prone and inefficient. The disclosed techniques enable the operator to review, analyze, filter, prioritize and extract useful information from large volumes of network traffic, even though the underlying protocols may not be decodable.

In alternative embodiments, system 20 may present the traffic segments to the operator using any other suitable technique. For example, if a given segment is detected more than once in the traffic, the system may present a summary that shows the segment only one (possibly together with the number of detected occurrences). In an example embodiment, the segments may be presented stream-by-stream, i.e., grouped by the traffic streams from which they originate. In an alternative embodiment, the segments can be presented jointly for all streams. For example, a recurring segment can be presented together with indications of the streams in which it appears. As yet another example, the system may present the original (“raw”) data and the decoded data in different colors.

Although the embodiments described herein mainly address test equipment applications, the principles of the present disclosure can also be used for QoS applications, DRM applications and various other applications.

It will thus be appreciated that the embodiments described above are cited by way of example, and that the present disclosure is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present disclosure includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art. 

The invention claimed is:
 1. A method, comprising: receiving in a computerized analysis system network traffic, which complies with a protocol, wherein the protocol is not decodable by the analysis system, and which network traffic carries a data item of a respective media type, wherein the data item comprises a sequence of bytes; automatically identifying the media type by processing the network traffic, wherein identifying the media type comprises identifying that the sequence of bytes comprises valid text; and extracting at least part of the data item responsively to the identified media type.
 2. The method according to claim 1, wherein identifying the valid text comprises identifying a character set that is used for constructing the sequence of bytes.
 3. The method according to claim 1, wherein identifying the valid text comprises verifying at least one criterion selected from a group of criteria consisting of: the sequence is longer than a predefined minimal length; at least a predefined portion of the bytes in the sequence represent letters; the sequence comprises only bytes representing valid displayable characters; and the sequence comprises at least one consecutive run of multiple alphanumerical characters.
 4. The method according to claim 1, wherein identifying the valid text comprises assessing a statistical property of the bytes, and identifying that the text is valid responsively to the assessed statistical property.
 5. The method according to claim 1, wherein assessing the statistical property comprises assessing an occurrence frequency of a given character in the sequence of the bytes.
 6. The method according to claim 1, wherein assessing the statistical property comprises assessing a measure of randomness of the sequence of the bytes.
 7. The method according to claim 1, wherein assessing the statistical property comprises comparing a first statistical distribution of the bytes in the sequence with a second statistical distribution of characters in a given language.
 8. The method according to claim 1, wherein identifying the media type comprises identifying in the network traffic a file type that is associated with the media type.
 9. The method according to claim 8, wherein identifying the file type comprises detecting a byte pattern that is characteristic of the file type.
 10. The method according to claim 1, wherein the data item comprises a sequence of bytes, and wherein identifying the media type comprises identifying that the sequence of bytes comprises visual information.
 11. The method according to claim 1, wherein the data item comprises a sequence of bytes, and wherein identifying the media type comprises identifying that the sequence of bytes comprises digitized audio.
 12. The method according to claim 1, wherein the data item comprises a sequence of bytes, and wherein identifying the media type comprises identifying that the sequence of bytes comprises encrypted information.
 13. The method according to claim 1, wherein the data item comprises a sequence of bytes, and wherein identifying the media type comprises identifying that the sequence of bytes comprises compressed information.
 14. The method according to claim 1, wherein extracting the data item comprises selecting a modality for presenting the data item responsively to the identified media type, and presenting the data item to an operator using the selected modality.
 15. Apparatus, comprising: an interface configured to receive network traffic, which complies with a protocol, wherein the protocol is not decodable by the analysis system, and which network traffic carries a data item of a respective media type, wherein the data item comprises a sequence of bytes; and a processor, which is configured to automatically identify the media type by processing the network traffic, wherein identifying the media type comprises identifying that the sequence of bytes comprises valid text, and to extract at least part of the data item responsively to the identified media type.
 16. The apparatus according to claim 15, wherein the processor is configured to identify the valid text by identifying a character set that is used for constructing the sequence of bytes. 